[scikit-learn] Micro average in classification report

[Andreas Mueller]

On 10/05/2018 12:00 PM, Kevin Markham wrote:
> Hello all,
>
> Congratulations on the release of 0.20! My questions are about the 
> updated classification_report: 
> http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
>
> Here is the simple example shown in the documentation (apologies for 
> the formatting):
>
> >>> from sklearn.metrics import classification_report
> >>> y_true = [0, 1, 2, 2, 2]
> >>> y_pred = [0, 0, 2, 2, 1]
> >>> target_names = ['class 0', 'class 1', 'class 2']
> >>> print(classification_report(y_true, y_pred, 
> target_names=target_names))
>               precision    recall  f1-score  support
>
>      class 0       0.50      1.00      0.67  1
>      class 1       0.00      0.00      0.00  1
>      class 2       1.00      0.67      0.80  3
>
>    micro avg       0.60      0.60      0.60  5
>    macro avg       0.50      0.56      0.49  5
> weighted avg       0.70      0.60      0.61  5
>
> I understand how macro average and weighted average are calculated. My 
> questions are in regard to micro average:
>
> 1. From this and other examples, it appears to me that "micro average" 
> is identical to classification accuracy. Is that correct?
>
> 2. Is there a reason that micro average is listed three times (under 
> the precision, recall, and f1-score columns)? From my understanding, 
> that 0.60 number is being calculated once but is being displayed three 
> times. The display implies (at least in my mind) that 0.60 is being 
> calculated from the three precision numbers, and separately calculated 
> from the three recall numbers, and separately calculated from the 
> three f1-score numbers, which seems misleading.
>
> 3. The documentation explains micro average as "averaging the total 
> true positives, false negatives and false positives". If my 
> understanding is correct that micro average is the same as accuracy, 
> then why are true negatives any less relevant to the calculation? 
> (Also, I don't mean to be picky, but "true positives" etc. are whole 
> number counts rather than rates, and so it seems odd to say that you 
> are arriving at a rate by averaging counts.)
>
> These may be dumb questions arising from my ignorance... my apologies 
> if so!

I had exactly the same comments and I find the current behavior 
confusing, see https://github.com/scikit-learn/scikit-learn/issues/12334
PR welcome!